Phishing attacks are rapidly evolving, fueled by advancements in artificial intelligence (AI). Generative AI tools have enabled attackers to create highly convincing scams that easily bypass traditional security measures. In this blog, we analyze a sophisticated phishing campaign intercepted by StrongestLayer’s Zero-Day Detection Engine—an AI-powered system designed to identify emerging threats in real time.
Unmasking the Attack: A Case Study
This phishing campaign went beyond the typical email scam, leveraging advanced techniques to manipulate victims and evade detection.
Step 1: The Setup—A Highly Convincing Fake
The attacker created a fraudulent website impersonating a well-known charity. Using AI-generated content, they precisely mimicked the charity’s branding—logos, color schemes, and even real-time donation updates—to create an illusion of legitimacy.
Step 2: Hijacking Contact Information
Instead of directly stealing credentials, the attacker swapped out legitimate contact details with their own VoIP-based phone numbers and email addresses. When victims attempted to inquire about donations, they unknowingly reached the scammer. This social engineering tactic bypassed traditional phishing filters, as no malicious links or downloads were required—victims were tricked through direct human interaction.
Step 3: Mass Email Distribution
Once the fake website was operational, the attacker launched a large-scale email campaign impersonating the charity. The emails appeared legitimate, using real marketing templates scraped from the actual charity’s website. These messages directed recipients to the scam site, where they were urged to donate or provide personal information.
AI-Powered Evasion: Why This Attack Was So Hard to Detect
What made this phishing campaign particularly dangerous was its use of AI-driven obfuscation techniques designed to evade security filters and threat analysts. Here are a couple of sample cases from the campaign:
1. Usage of AI assisted encoding and replicating tools
Using AI assisted tools with multi-layer encoding of the landing page with random text quotes embedded in the metadata part of the webpage so that multiple replicas of the exact same website can be spun up seamlessly across multiple websites without getting detected by duplicate website detection algorithms that are in place on many domain hosting providers to detect potential phishing campaigns. The multi-layer encoding part helps these websites evade signature based detection that might be written based on the landing page text which in some cases included exact same reviews from exact same people, using the exact same words.
2. Using platforms like Google Tag Manager for malicious code hiding
Instead of embedding malicious content directly into the webpage, the attackers leveraged Google Tag Manager (GTM)—a legitimate tool used for managing website analytics and tracking. This allowed them to dynamically inject scam-related content only when accessed by specific users.
Why it’s dangerous:
Traditional phishing detection relies on static analysis of website content. By using GTM, the scammer could keep the site looking clean to security crawlers while dynamically serving phishing content to real users.
How StrongestLayer caught it:
Our recursive-predictive AI based detection models (Zero-Day Detection Engine or ZDE) analyze behavioral anomalies and then correlate them with a very large number of malicious artifacts gathered by StrongestLayer over the years, bringing out the threat actors which are then tracked by the ZDE to monitor their nefarious activities online, rather than just scanning for known threats.
In this case, the threat actor was known by StrongestLayer for many months at this point. On top of that, StrongestLayer’s Threat Intelligence backend detected suspicious JavaScript calls being made from within the encoded scripts as the already detected phishing site became armed and operational, making it a double whammy for the phishing site.
3. AI-Generated Website Cloning and Content Manipulation
The attacker used generative AI tools to clone legitimate websites, ensuring the phishing page was a near-exact replica of the real charity’s site. They also embedded randomized metadata text, such as inspirational quotes, to avoid detection by anti-phishing engines that flag duplicate site templates.
- Why it’s dangerous: Many phishing detection tools rely on fingerprinting techniques to identify copied content. AI-generated variations prevent these detections.
- How StrongestLayer caught it: Our threat models analyze underlying HTML structures, cross-referencing them with behavioral patterns rather than relying solely on static content matches.
4. Multi-Layered Code Obfuscation
The phishing site’s HTML and JavaScript code was deeply obfuscated using multiple layers of encryption and encoding techniques. This prevented human analysts and automated scanners from easily identifying malicious scripts.
- Why it’s dangerous: Security teams often use reverse-engineering techniques to analyze malicious websites. Heavily obfuscated code adds complexity, slowing down threat response times.
- How StrongestLayer caught it: Our AI models identified irregular execution patterns and flagged anomalies in the way scripts loaded dynamically across different user sessions.
Why Traditional Security Measures Are Failing
Most legacy security solutions rely on:
✅ Signature-based detection – Checking URLs and attachments against known blacklists
✅ Static pattern recognition – Identifying repeated attack behaviors
✅ Manual analysis – Relying on human analysts to investigate threats
However, AI-powered phishing campaigns are designed to change dynamically and evade these outdated techniques. Attackers are now using the same AI tools as cybersecurity professionals—but for malicious purposes.
Why AI-Powered Threat Detection Is Essential
This case highlights why traditional email security solutions are no longer enough. Organizations need adaptive, AI-driven defenses to detect zero-day threats—attacks that are entirely new and have no prior signatures.
How StrongestLayer Stops These Attacks in Real Time
✔ Hybrid AI Models – Combining generative AI analysis with behavioral threat detection
✔ Zero-Day Detection Engine – Identifies threats based on execution patterns, not static signatures
✔ Real-Time Behavioral Monitoring – Flags anomalies like obfuscated code execution and GTM-based content swapping
By leveraging AI for real-time detection, StrongestLayer ensures that even the most sophisticated phishing scams are neutralized before they reach your inbox.
The AI Arms Race in Cybersecurity
AI has transformed cybersecurity—for both defenders and attackers. As phishing threats grow more advanced, businesses must shift from reactive security to proactive AI-driven defense.
StrongestLayer is at the forefront of this fight, ensuring organizations stay ahead of evolving threats. In today’s digital battlefield, adopting AI-powered security isn’t just an advantage—it’s a necessity.